Nonlocal patch-based label fusion for hippocampus segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6363 LNCS, Issue PART 3, 2010, Pages 129-136

  Coupé, Pierrick ^a,b   Manjón, José V ^c   Fonov, Vladimir ^a,b   Pruessner, Jens ^a,b   Robles, Montserrat ^c   Collins, D Louis ^a,b  

^a MCGILL UNIVERSITY   (Canada)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

^c UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

hippocampus segmentation; nonlocal means estimator

Indexed keywords

ANATOMICAL STRUCTURES; APPEARANCE-BASED METHODS; AUTOMATIC EXTRACTION; CEREBRAL STRUCTURES; FUSION STRATEGIES; HEALTHY SUBJECTS; HIPPOCAMPUS SEGMENTATION; IMAGE DE-NOISING; KAPPA VALUES; NONLOCAL; PATCH SIZE; QUANTITATIVE MAGNETIC RESONANCES; ROBUST SEGMENTATION; SEGMENTATION ACCURACY; SEGMENTATION PRIOR; TEMPLATE-BASED METHOD;

MAGNETIC RESONANCE; MEDICAL COMPUTING; MEDICAL IMAGING;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; HIPPOCAMPUS; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; HIPPOCAMPUS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 84883845354     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15711-0_17     Document Type: Conference Paper

References (17)

1. Chupin, M., et al.: Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer's disease. NeuroImage 34(3), 996-1019 (2007)
2. Shen, D., et al.: Measuring size and shape of the hippocampus in MR images using a deformable shape model. NeuroImage 15(2), 422-434 (2002) (Pubitemid 34855025)
3. Hu, S., Collins, D.L.: Joint level-set shape modeling and appearance modeling for brain structure segmentation. NeuroImage 36(3), 672-683 (2007) (Pubitemid 46907612)
4. Duchesne, S., Pruessner, J., Collins, D.L.: Appearance-based segmentation of medial temporal lobe structures. NeuroImage 17(2), 515-531 (2002) (Pubitemid 35316992)
5. Barnes, J., et al.: A comparison of methods for the automated calculation of volumes and atrophy rates in the hippocampus. NeuroImage 40(4), 1655-16571 (2008)
6. Heckemann, R.A., et al.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1), 115-126 (2006)
7. Aljabar, P., et al.: Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. NeuroImage 46(3), 726-738 (2009)
8. Hammers, A., et al.: Automatic detection and quantification of hippocampal atrophy on MRI in temporal lobe epilepsy: a proof-of-principle study. NeuroImage 36(1), 38-47 (2007) (Pubitemid 46629143)
9. Collins, D., Pruessner, J.: Towards Accurate, Automatic Segmentation of the Hippocampus and Amygdala from MRI. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 592-600. Springer, Heidelberg (2009)
10. Lotjonen, J.M., et al.: Fast and robust multi-atlas segmentation of brain magnetic resonance images. NeuroImage 49(3), 2352-2365 (2010)
11. Gousias, I.S., et al.: Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest. NeuroImage 40(2), 672-684 (2008)
12. Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60-65 (2005)
13. Coupe, P., et al.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imaging 27(4), 425-441 (2008)
14. Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging 17(1), 87-97 (1998)
15. Collins, D.L., et al.: Automatic 3-D model-based neuroanatomical segmentation. Human Brain Mapping 3(3), 190-208 (1995)
16. Nyul, L.G., Udupa, J.K.: Standardizing the MR image intensity scales: making MR intensities have tissue specific meaning. Medical Imaging 2000: Image Display and Visualization 1(21), 496-504 (2000)
17. Wang, Z., et al.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600-612 (2004)